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Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have ov...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653582/ https://www.ncbi.nlm.nih.gov/pubmed/37936309 http://dx.doi.org/10.1093/gbe/evad201 |
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author | Abakarova, Marina Marquet, Céline Rera, Michael Rost, Burkhard Laine, Elodie |
author_facet | Abakarova, Marina Marquet, Céline Rera, Michael Rost, Burkhard Laine, Elodie |
author_sort | Abakarova, Marina |
collection | PubMed |
description | The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline. |
format | Online Article Text |
id | pubmed-10653582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106535822023-11-04 Alignment-based Protein Mutational Landscape Prediction: Doing More with Less Abakarova, Marina Marquet, Céline Rera, Michael Rost, Burkhard Laine, Elodie Genome Biol Evol Letter The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline. Oxford University Press 2023-11-04 /pmc/articles/PMC10653582/ /pubmed/37936309 http://dx.doi.org/10.1093/gbe/evad201 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Letter Abakarova, Marina Marquet, Céline Rera, Michael Rost, Burkhard Laine, Elodie Alignment-based Protein Mutational Landscape Prediction: Doing More with Less |
title | Alignment-based Protein Mutational Landscape Prediction: Doing More with Less |
title_full | Alignment-based Protein Mutational Landscape Prediction: Doing More with Less |
title_fullStr | Alignment-based Protein Mutational Landscape Prediction: Doing More with Less |
title_full_unstemmed | Alignment-based Protein Mutational Landscape Prediction: Doing More with Less |
title_short | Alignment-based Protein Mutational Landscape Prediction: Doing More with Less |
title_sort | alignment-based protein mutational landscape prediction: doing more with less |
topic | Letter |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653582/ https://www.ncbi.nlm.nih.gov/pubmed/37936309 http://dx.doi.org/10.1093/gbe/evad201 |
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